The INS is a widely used technology for guidance and navigation of a vehicle. The INS is composed of an inertial measurement unit (IMU) and a processor wherein an IMU houses accelerometers and gyroscopes which are inertial sensors for detecting platform motion with respect to an inertial coordinate system. An important advantage of the INS is independence from external support, i.e., it is self-contained. However, the INS cannot provide high accuracy at long ranges. This is because inertial sensors are subject to errors that tend to accumulate over time, i.e., the longer the drive time, the greater the inaccuracy.
More recent development in global positioning system (GPS) has made high accuracy vehicle navigation possible at low cost. However, since the GPS relies on GPS satellites, it is susceptible to jamming, RF (radio frequency) interference and multipath problems. Although the GPS provides accurate position and velocity over longer time periods, the GPS involves occasional large multipath errors and signal dropouts. Therefore, efforts are made to develop integrated INS/GPS navigation systems by combining the outputs of a GPS and an INS using the Kalman filter to remedy performance problems of both systems.
Inertial sensors used to be expensive and bulky, thus only used in precision application, e.g., aerospace and military navigation. For establishing an IMU package in a compact and inexpensive manner, efforts have been made to develop MEMS sensors resulting in commercialization of low-cost, small, but noisier MEMS inertial sensors. A computational scheme for an INS commonly known in the art offers an exact formula applicable to a system with high-end inertial sensors, for example, ring laser gyros, to track the platform six degrees of freedom without any conditions in the platform dynamics ([1] Titterton, D. H. and Weston, J. L., “Strapdown Inertial Navigation Technology”, Peter Peregrinus Ltd., Stevenage, Herts., England, U. K., 1997, [2] Savage, P. G., “Strapdown Analytics”, Strapdown Associates, Inc., Maple Plain, MN, 2000).
Because of the large amount of noise and bias, however, use of the conventional formula for a system with low-cost MEMS sensors is unnecessarily detailed and often results in numerical instability which often happens as soon as GPS signals are lost. Also, assumptions of free orientation, free speed, and free altitude made in the conventional method result in unnecessary computation for most of commercial vehicles whose pitch and roll angles are restricted within −90 to +90 degrees with low speed and altitude.
Therefore, there is a need of a new computational method for a MEMS based INS/GPS for larger stability and greater efficiency assuming larger sensor errors and restrictions in the platform dynamics.